1 About

This data notebook is based on a model presented at the 2021 International Conference on Evolving Cities, University of Southampton, 22 – 24 September 2021.

If you want to cite the method/model please use:

Anderson, B. (2021). Simulating the consequences of an emissions levy at the city and neighbourhood scale. Paper presented at the International Conference on Evolving Cities, MAST Mayflower Studios, Southampton, United Kingdom. 22 - 24 Sep 2021.

If you are interested in how the model works start from https://dataknut.github.io/localCarbonTaxModels/

2 Citing this data notebook

If you wish to re-use material from this data notebook please cite it as:

Ben Anderson (2021) Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit: All English LSOAs. University of Southampton, United Kingdom

License: CC-BY

Share, adapt, give attribution.

3 Highlights

This data notebook estimates the value of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator. These emissions are all consumption, gas and electricity. It does this under two scenarios - a simple carbon value multiplier and a rising block tariff.

It then compares these with estimates of the cost of retrofitting EPC band dwellings D-E and F-G in each LSOA and for the whole area under study.

Key results:

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e - which areas emit the most?
Table 4.6 shows total levy generated under Scenario 1
Table 4.12 shows total levy generated under Scenario 2
Figure 4.16 compares the scenarios in terms of % of levy generated by areas in each IMD decile while Figure 4.17 compares the levy generated under each scenario at LSOA level. In both cases, Scenario 2 should be lower in more deprived areas and higher in less deprived areas.
Table 4.13 shows total retrofit costs and Figure 4.19 shows the LSOA level retrofit costs per dwelling by IMD decile for comparison with Figure 4.1
Figure 4.20 shows the years to pay back under Scenario 1 for an all emissions levy while * Figure 4.24 does the same for Scenario 2
Figure 4.22 shows what would happen after year 1 if the levy were shared equally across LSOAs (all emissions, Secenario 1) and Figure 4.26 shows the same for Scenario 2.
Figure 4.28 shows payback years under each Scenario assuming a constant all emissions levy

4 Emissions Levy Case Study: All English LSOAs

This data notebook estimates a model of an emissions levy using LSOA level data on emissions derived from the CREDS place-based emissions calculator.

The model applies carbon ‘values’ to a number of emissions categories to estimate the levy revenue that would be generated for each LSOA in year 1 of such a levy. It then sums these values to given an overall levy revenue estimate for the area in the case study.

The data notebook then use estimates of the cost of retrofitting EPC band dwellings D-E and F-G together with estimates of the number of such dwellings in each of the LSOAs to calculate the likely cost of such upgrades in each LSOA and for the whole area in the case study.

Finally the data notebook compares the distributions of the two to understand whether sufficient revenue would be generated within each LSOA to enable the per-LSOA or whole case study area costs of the energy efficiency upgrades to be met. In doing so the data notebook also analyses the extent to which redistribution of revenue from high emissions areas (households) would be required.

It should be noted that this area level analysis uses mean emissions per household. It will therefore not capture within-LSOA heterogeneity in emissions and so will almost certainly underestimate the range of the household level emissions levy values that might be expected.

NB: no maps in the interests of speed

4.1 Data

The model uses a number of Lower Layer Super Output Area (LSOA) level datasets to analyse the patterns of emissions.

CREDS place-based emissions estimates (contains data from a wide range of sources including https://epc.opendatacommunities.org/ for EPCs)
2018 residential gas & electricity use data
Indices of Deprivation 2019
Urban/Rural classification at LSOA level
various LSOA look-up tables from geoportal

All analysis is at LSOA level. Cautions on inference from area level data apply.

4.2 LSOA level emissions estimates

See https://www.creds.ac.uk/why-we-built-a-place-based-carbon-calculator/

“The highest carbon areas have an average per person footprint more than eight times larger than the lowest carbon areas.”

“We are not effectively targeting decarbonisation policies in high carbon areas. For example, the recently collapsed Green Homes Grants scheme provided a grant to cover 66% of the cost (up to £5,000) of retrofitting homes. For people claiming certain benefits, the cap was raised to 100% and £10,000. But the calculator shows that the big polluters are the large homes in very wealthy areas. In these neighbourhoods, the issue is not affordability but motivation. For high income households, energy costs are a small proportion of their expenditure and so the cost savings for retrofitting their home are inconsequential. As there are no policy “sticks” to incentivise action in the collective interest it is unsurprising that high carbon neighbourhoods have not prioritised decarbonisation."

Source: https://www.carbon.place/

Notes:

Emissions are presented as per capita…
Appears to be based on residential/citizen emissions only - does not appear to include commercial/manufacturing/land use etc

Table 4.1: Summary of CREDS per capita data for LSOAs: All English LSOAs
region	nLSOAs	mean_KgCo2ePerCap	sd_KgCo2ePerCap
	805	9,834.6	2,737.8
East	3,392	8,886.3	3,134.0
East Midlands	2,713	7,835.8	3,195.0
London	4,826	9,116.2	3,137.5
North East	1,634	6,801.0	4,123.3
North West	4,463	7,430.7	2,862.8
South East	5,278	9,813.2	3,648.3
South West	3,059	7,930.2	2,780.2
West Midlands	3,403	7,506.7	4,037.7
Yorkshire and The Humber	3,271	7,419.2	2,772.2

Now we need to convert the per capita to totals and then use the number of electricity meters as a proxy for the number of dwellings

Ideally we’d have Census 2021 data but we don’t have it yet. So instead we’ll use the number of electricity meters for 2018 which aligns with the CREDS data (might be an over-estimate if a dwelling has 2…)

First check the n electricity meters logic…

##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 2:    Test Valley 003B              St Mary's       2641        2487      2230
## 3:  Milton Keynes 017H              Broughton       2517        2382      2460
## 4:    Test Valley 003A                Alamein       2513        2638      2350
## 5:   Peterborough 019D       Stanground South       2261        2178      1880
## 6:        Swindon 008B Blunsdon and Highworth       2227        2166      2020

##               LSOA11NM                 WD18NM nGasMeters nElecMeters epc_total
## 1:         Newham 013G Stratford and New Town        731        6351      6350
## 2:     Wandsworth 002B             Queenstown        675        3282      1700
## 3: Aylesbury Vale 012A              Riverside       3373        3175      3110
## 4:         Newham 037E            Royal Docks        574        3116      2900
## 5:       Lewisham 012E       Lewisham Central        568        2893      2730
## 6:    Test Valley 003A                Alamein       2513        2638      2350

(#tab:check_nElecMeters)LSOAs with highest number of gas meters (after cleaning)
LSOA11NM	WD18NM	nGasMeters	nElecMeters	epc_total
Aylesbury Vale 012A	Riverside	3,373	3,175	3,110
Test Valley 003B	St Mary's	2,641	2,487	2,230
Milton Keynes 017H	Broughton	2,517	2,382	2,460
Test Valley 003A	Alamein	2,513	2,638	2,350
Peterborough 019D	Stanground South	2,261	2,178	1,880
Swindon 008B	Blunsdon and Highworth	2,227	2,166	2,020

Check that the number of electricity meters reasonably correlates with the number of EPCs from the CREDS data. We would not expect the number of gas meters to correlate due to non-gas dwellings etc.

There may also be difficulties where there are multiple meters per property - e.g. one ‘standard’ and one ‘economy 7.’ Really should switch to using address counts from postcode file.

Check that the assumption seems sensible…

Check for outliers - what might this indicate?

4.2.1 Estimate per dwelling emissions

We want to present the analysis in ‘per dwelling’ or ‘per household’ terms so we need to convert the total kg CO2e values to per dwelling values by dividing by the number of electricity meters.

## # Summary of per dwelling values

Table 4.2: Data summary
Name	…[]
Number of rows	32039
Number of columns	9
Key	NULL
_______________________
Column type frequency:
numeric	9
________________________
Group variables	None

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
CREDStotal_kgco2e_pdw	0	1	19490.24	9188.71	3587.62	12947.16	18275.82	24069.71	586372.22	▇▁▁▁▁
CREDSgas_kgco2e2018_pdw	0	1	2465.42	851.99	3.92	2037.62	2434.68	2868.68	71095.56	▇▁▁▁▁
CREDSelec_kgco2e2018_pdw	0	1	1021.63	220.17	40.55	888.82	977.15	1092.44	4046.23	▂▇▁▁▁
CREDSmeasuredHomeEnergy_kgco2e2018_pdw	0	1	3487.05	912.74	458.61	2978.41	3398.85	3894.92	72698.53	▇▁▁▁▁
CREDSotherEnergy_kgco2e2011_pdw	0	1	175.08	336.37	0.00	40.20	69.74	136.09	6877.09	▇▁▁▁▁
CREDSallHomeEnergy_kgco2e2018_pdw	0	1	3662.13	910.23	912.57	3125.71	3558.65	4082.50	76436.03	▇▁▁▁▁
CREDScar_kgco2e2018_pdw	0	1	2200.93	1038.37	127.70	1529.01	2142.16	2797.44	89700.00	▇▁▁▁▁
CREDSvan_kgco2e2018_pdw	1	1	366.16	2774.12	0.05	137.01	217.71	342.60	344822.80	▇▁▁▁▁
CREDSpersonalTransport_kgco2e2018_pdw	1	1	2567.13	2987.05	141.80	1742.05	2422.45	3151.56	346819.80	▇▁▁▁▁

Examine patterns of per dwelling emissions for sense.

4.2.1.1 All emissions

Figure 4.1 shows the LSOA level per dwelling ‘all emissions’ in Tonnes CO2e as estimated by the CREDS tool against the Index of Multiple Deprivation (IMD) score and uses the size of the points to represent the % of dwellings with electric heating. Colour is used to represent the IMD decile where decile 1 is the 10% least deprived.

## Per dwelling T CO2e - all emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.1: Scatter of LSOA level all consumption emissions per dwelling against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDStotal_kgco2e_pdw
## t = -123.51, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5753081 -0.5604715
## sample estimates:
##        cor 
## -0.5679359

## Total emissions per dwelling (LSOA level) summary

##    LSOA11CD            WD18NM                         IMD_Decile_label All_Tco2e_per_dw 
##  Length:32039       Length:32039       1 (10% most deprived)  : 3282   Min.   :  3.588  
##  Class :character   Class :character   10 (10% least deprived): 3280   1st Qu.: 12.947  
##  Mode  :character   Mode  :character   2                      : 3271   Median : 18.276  
##                                        9                      : 3247   Mean   : 19.490  
##                                        3                      : 3237   3rd Qu.: 24.070  
##                                        8                      : 3220   Max.   :586.372  
##                                        (Other)                :12502

Table 4.3: Highest emitting LSOAs (per dwelling)
LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01031998	Durrington and Larkhill	9	586.4
E01009320	Sheldon	4	364.7
E01033484	Park East	1 (10% most deprived)	203.7
E01010151	Knowle	8	171.2
E01019556	Holmebrook	2	160.2
E01033749	Greenbank	6	139.7

Table 4.3: Lowest emitting LSOAs (per dwelling)
LSOA11CD	WD18NM	IMD_Decile_label	All_Tco2e_per_dw
E01033583	Stratford and New Town	3	3.6
E01033726	Eltham West	2	3.8
E01015895	Victoria	1 (10% most deprived)	4.3
E01008703	Hendon	1 (10% most deprived)	4.4
E01005133	Ancoats & Beswick	1 (10% most deprived)	4.9
E01004562	Queenstown	3	5.0

4.2.1.2 Home energy use

Figure 4.2 uses the same plotting method to show emissions per dwelling due to gas use.

## Per dwelling T CO2e - gas emissions

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     3.92  2037.62  2434.68  2465.42  2868.68 71095.56

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.2: Scatter of LSOA level gas per dwelling emissions against IMD score

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSgas_kgco2e2018_pdw
## t = -70.089, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3740796 -0.3550910
## sample estimates:
##        cor 
## -0.3646232

Figure 4.3 uses the same plotting method to show emissions per dwelling due to electricity use.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.3: Scatter of LSOA level elec per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058

Figure 4.4 uses the same plotting method to show emissions per dwelling due to other energy use. This should be higher for off-gas areas which tend to be rural areas so we also present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - elec emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.4: Scatter of LSOA level other energy per dwelling emissions against IMD score - who emits?

## Correlation test (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSelec_kgco2e2018_pdw
## t = -77.608, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4069829 -0.3885483
## sample estimates:
##        cor 
## -0.3978058

Table 4.4: Summary of per dwelling energy emissions for LSOAs by urban/rural code
RUC11	mean_gas_kgco2e	mean_elec_kgco2e	mean_other_energy_kgco2e
Rural town and fringe	2,536.8	1,083.0	274.2
Rural town and fringe in a sparse setting	2,254.1	993.7	271.6
Rural village and dispersed	1,879.3	1,481.9	1,131.9
Rural village and dispersed in a sparse setting	1,015.1	1,405.2	1,440.1
Urban city and town	2,456.0	991.9	86.3
Urban city and town in a sparse setting	2,230.2	945.0	124.6
Urban major conurbation	2,552.2	981.3	108.7
Urban minor conurbation	2,582.8	913.9	124.0

Check whether all measured energy emissions combined (gas & electricity) correlate with all emissions (in this data).

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSmeasuredHomeEnergy_kgco2e2018_pdw
## t = 158.14, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6559143 0.6682142
## sample estimates:
##       cor 
## 0.6621088

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Do we see strong correlations? If so in theory we could (currently) use measured energy emissions as a proxy for total emissions.

Repeat for all home energy - includes estimates of emissions from oil etc

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$CREDStotal_kgco2e_pdw and selectedLsoasDT$CREDSallHomeEnergy_kgco2e2018_pdw
## t = 177.83, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.6992585 0.7102801
## sample estimates:
##       cor 
## 0.7048118

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

How does the correlation look now?

4.2.1.3 Transport

We don’t expect to use this data as it is already taxed in a way that relates to emissions (?)

Figure 4.5 uses the same plotting method to show emissions per dwelling due to van use. Again, we present analysis by the LSOA’s urban/rural classification.

## Per dwelling T CO2e - car emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.5: Scatter of LSOA level car use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDScar_kgco2e2018_pdw
## t = -119.05, df = 32037, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.5613723 -0.5461891
## sample estimates:
##        cor 
## -0.5538267

Table 4.5: Summary of per dwelling transport emissions for LSOAs by urban/rural code
RUC11	mean_car_kgco2e	mean_van_kgco2e
Rural town and fringe	2,882.6	412.8
Rural town and fringe in a sparse setting	2,198.1	347.0
Rural village and dispersed	3,754.9	664.4
Rural village and dispersed in a sparse setting	3,095.9	586.6
Urban city and town	2,280.4	379.3
Urban city and town in a sparse setting	1,761.6	300.2
Urban major conurbation	1,719.0
Urban minor conurbation	1,899.4	307.6

Figure 4.6 uses the same plotting method to show emissions per dwelling due to van use.

## Per dwelling T CO2e - van emissions

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.6: Scatter of LSOA level van use per dwelling emissions against IMD score

## Correlation with IMD score (pwcorr)

## 
##  Pearson's product-moment correlation
## 
## data:  selectedLsoasDT$IMDScore and selectedLsoasDT$CREDSvan_kgco2e2018_pdw
## t = -0.79155, df = 32036, p-value = 0.4286
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.015371712  0.006528074
## sample estimates:
##          cor 
## -0.004422349

4.2.2 Estimating the annual emissions levy

Case studies:

Annual carbon tax
Half-hourly (real time) carbon tax (not implemented) - this would only affect electricity

BEIS/ETC Carbon ‘price’

2020 - £21.93 https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/849276/Carbon__Penalty_Price_Determination_for_2020.pdf

EU carbon ‘price’

September 2021: €60 (£51) https://tradingeconomics.com/eecxm:ind

BEIS Carbon ‘Value’ https://www.gov.uk/government/publications/valuing-greenhouse-gas-emissions-in-policy-appraisal/valuation-of-greenhouse-gas-emissions-for-policy-appraisal-and-evaluation#annex-1-carbon-values-in-2020-prices-per-tonne-of-co2

based on a Marginal Abatement Cost (MAC)
2021:
- Low: £122/T
- Central: £245/T <- use the central value for now
- High: £367/T

Scenario 1: apply the central value Scenario 2: apply the low/central/high as a rising block tariff for each emissions source. Set threhsolds to 33% and 66% (in absence of any other guidance!)

4.2.2.1 Scenario 1: Central cost

Table 4.6 below shows the overall £ GBP total for the case study area in £M under Scenario 1.

Table 4.6: Total £m GBP levy by source
nLSOAs	beis_GBPtotal_c	beis_total_c_gas	beis_GBPtotal_c_elec
32,039	107,100.4	13,847.3	5,871.4

Table 4.6: Total £m GBP levy by source, ordered by highest emitting region
region	nLSOAs	beis_GBPtotal_c	beis_total_c_gas	beis_GBPtotal_c_elec
South East	5,278	20,772.1	2,272.1	1,038.8
London	4,826	19,038.1	2,048.9	870.5
North West	4,463	12,703.6	1,952.4	766.1
East	3,392	12,199.3	1,450.0	668.6
West Midlands	3,403	10,191.7	1,475.8	604.8
South West	3,059	9,784.3	1,147.2	613.9
Yorkshire and The Humber	3,271	9,495.3	1,494.1	550.2
East Midlands	2,713	8,687.1	1,249.5	503.9
North East	1,634	4,228.9	757.3	254.7

Figure 4.7: Proportion of total emissions due to gas & electricity use by region covered

The table below shows the mean per dwelling value rounded to the nearest £10.

(#tab:GBP_beis_c_perdw)Mean £ GBP levy per dwelling by source
All_emissions	Gas	Electricity	Gas + Electricity
4,775.1	604.0	250.3	854.3

Figure ?? shows the total £k per LSOA and £ per dwelling revenue using BEIS central carbon price plotted against IMD score. The tables show the LSOAs with the highest and lowest values.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.8: £k per LSOA revenue using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.9: £k per LSOA revenue using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     879    3172    4478    4775    5897  143661

Table 4.7: Highest levy LSOAs
LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01031998	Wiltshire 045C	Durrington and Larkhill	586.4	143,661.2
E01009320	Birmingham 081F	Sheldon	364.7	89,343.8
E01033484	Darlington 008F	Park East	203.7	49,897.4
E01010151	Solihull 026A	Knowle	171.2	41,947.7
E01019556	Chesterfield 010C	Holmebrook	160.2	39,241.7
E01033749	Liverpool 042F	Greenbank	139.7	34,224.3

Table 4.7: Lowest levy LSOAs
LSOA11CD	LSOA01NM	WD18NM	T_CO2e_pdw	GBP_allEmissions_levy
E01033583	Newham 013G	Stratford and New Town	3.6	879.0
E01033726	Greenwich 034E	Eltham West	3.8	933.1
E01015895	Southend-on-Sea 010A	Victoria	4.3	1,050.9
E01008703	Sunderland 013B	Hendon	4.4	1,070.5
E01005133	Manchester 013D	Ancoats & Beswick	4.9	1,202.1
E01004562	Wandsworth 002B	Queenstown	5.0	1,216.5

Figure ?? repeats the analysis but just for gas.

Anything unusual?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.10: £k per LSOA incurred via gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.11: £k per LSOA incurred via gas using BEIS central carbon price

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##     0.96   499.22   596.50   604.03   702.83 17418.41

Table 4.8: Highest emitting by £ GBP gas levy
LSOA11CD	LSOA01NM	WD18NM	gas_T_CO2e_pdw	GBP_gas_levy_perdw
E01031998	Wiltshire 045C	Durrington and Larkhill	71.1	17,418.4
E01000213	Barnet 033F	Garden Suburb	7.4	1,802.1
E01023812	Three Rivers 004A	Chorleywood North & Sarratt	7.2	1,756.1
E01023841	Three Rivers 011C	Moor Park & Eastbury	6.9	1,696.8
E01004114	Sutton 025D	Cheam	6.7	1,646.7
E01023813	Three Rivers 004B	Chorleywood North & Sarratt	6.7	1,646.1

Table 4.8: Lowest emitting by £ GBP gas levy
LSOA11CD	LSOA01NM	WD18NM	GBP_gas_levy_perdw
E01026645	King's Lynn and West Norfolk 002A	Brancaster	3.9
E01026718	King's Lynn and West Norfolk 004D	Valley Hill	3.5
E01027382	Northumberland 002D	Norham and Islandshires	3.3
E01020864	County Durham 064G	Evenwood	3.2
E01032746	Southampton 029F	Bargate	3.1
E01020534	West Dorset 003F	Maiden Newton	1.0

Figure ?? repeats the analysis for electricity.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.12: £k per LSOA incurred via electricity using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.13: £k per LSOA incurred via electricity using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   9.934 217.760 239.403 250.299 267.648 991.327

Table 4.9: Highest emitting by £ GBP electricity levy
LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw
E01000206	Barnet 033B	Garden Suburb	4.0	991.3
E01030692	Runnymede 005D	Virginia Water	3.4	823.2
E01030342	Elmbridge 018B	Oxshott and Stoke D'Abernon	3.3	819.8
E01030346	Elmbridge 016A	Weybridge St George's Hill	3.3	803.8
E01004690	Westminster 019D	Knightsbridge and Belgravia	2.9	704.6
E01003465	Merton 002D	Village	2.9	703.9

Table 4.9: Lowest emitting by £ GBP electricity levy
LSOA11CD	LSOA01NM	WD18NM	elec_T_CO2e_pdw	GBP_elec_levy_perdw
E01008777	Sunderland 026C	St Chad's	0.5	124.5
E01024604	Swale 014C	St Ann's	0.5	118.3
E01002862	Kensington and Chelsea 014E	Stanley	0.5	111.4
E01033736	Greenwich 004H	Woolwich Riverside	0.4	106.3
E01004739	Westminster 024E	Tachbrook	0.3	81.4
E01010257	Walsall 007E	Aldridge North and Walsall Wood	0.0	9.9

Figure ?? shows the same analysis for measured energy (elec + gas)

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.14: £k per LSOA incurred via electricity and gas using BEIS central carbon price

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.15: £k per LSOA incurred via electricity and gas using BEIS central carbon price

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   112.4   729.7   832.7   854.3   954.3 17811.1

4.2.2.2 Scenario 2: Rising block tariff

Applied to per dwelling values (not LSOA total) - may be methodologically dubious?

Cut at 25%, 50% - so any emissions over 50% get high carbon cost

## Cuts for total per dw

##         0%        25%        50%        75%       100% 
##   3587.624  12947.165  18275.816  24069.709 586372.222

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Table: (#tab:estimateAnnualLevyScenario2Total)Data summary

Name	…[]
Number of rows	32039
Number of columns	3
Key	NULL
_______________________
Column type frequency:
numeric	3
________________________
Group variables	None

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
V1	1	19.49	9.19	3.59	12.95	18.28	24.07	586.37	▇▁▁▁▁
beis_GBPtotal_sc2_perdw	1	3727.91	3031.87	437.69	1579.54	2885.00	5011.43	211376.45	▇▁▁▁▁
beis_GBPtotal_sc2	1	2528423.89	1653321.47	543095.20	1222262.12	2220433.98	3356216.64	84377314.16	▇▁▁▁▁

Table 4.11: Compare totals for Scenario 1 & Scenario 2 (£m)
nLSOAs	sum_total_sc1	sum_total_sc2
32,039	107,100.4	81,008.2

4.2.3 Compare scenarios

Figure 4.16 compares the % £ levy under each scenario for all consumption contributed by LSOAs in each IMD decile.

Figure 4.16: Comparing £ levy under each scenario by IMD decile - all consumption emissions

Figure 4.17 compares the £ levy under each scenario for all consumption.

Figure 4.17: Comparing £ levy under each scenario - all consumption emissions

## [1] 9086.681

## [1] 3997.045

Table 4.12: Scenario 2 totals for: All English LSOAs (£ m)
nLSOAs	sumAllConsEmissions_GBP	sumGasEmissions_GBP	sumElecEmissions_GBP
32,039	81,008.2	9,086.7	3,997.0

Table 4.12: Scenario 2 totals by region - if multiple regions (£ m)
region	nLSOAs	sumAllConsEmissions_GBP	sumGasEmissions_GBP	sumElecEmissions_GBP	sumPop
South East	5,278	17,507.7	1,503.9	766.7	8,973,952
London	4,826	15,649.7	1,389.4	583.8	8,889,572
East	3,392	9,526.3	939.1	487.4	5,818,700
North West	4,463	8,777.3	1,282.6	491.5	7,236,660
West Midlands	3,403	7,270.4	976.6	410.8	5,765,703
South West	3,059	6,810.0	654.0	431.1	5,213,266
Yorkshire and The Humber	3,271	6,504.8	1,011.1	342.9	5,405,939
East Midlands	2,713	6,247.7	827.1	339.5	4,693,551
North East	1,634	2,714.3	502.9	143.4	2,622,240

Figure 4.18: Contribution to sum levy £ GBP by source

4.3 Estimate retofit costs

from A-E <- £13,300
from F-G <- £26,800

Source: English Housing Survey 2018 Energy Report

Model excludes EPC A, B & C (assumes no need to upgrade)

Adding these back in would increase the cost… obvs

4.3.1 Impute EPC counts

In order to estimate the LSOA level retrofit costs, we need to impute the EPC counts in each LSOA. We do this using the number of electricity meters as the presumed number of dwellings and the observed % of EPCs in each band for all dwellings with EPCs which is provided by the CREDS data. This assumes that if we had EPCs for all dwellings then the % in each band in each LSOA would stay the same. This is quite a bold assumption…

Note that the EPC database is continuously updated so more recent upgrades will not be captured in the data used for this analysis. This means the total retrofit costs are likely to be an over-estimate. The extent of this over-estimate would require the use of an updated (current) EPC data extract and is left for future work.

## N EPCs

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    30.0   315.0   390.0   434.2   503.0  6350.0

## N elec meters

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    36.0   623.0   692.0   736.3   809.0  6351.0

Correlation between high % EPC F/G or A/B and deprivation?

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Now we need to convert the % to dwellings using the number of electricity meters (see above).

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.3.2 Estimate costs

Table 4.13 reports total retofit costs.

## To retrofit D-E (£m)

## [1] 177847.9

## Number of dwellings: 13372024

## To retrofit F-G (£m)

## [1] 26752.52

## Number of dwellings: 998229

## To retrofit D-G (£m)

## [1] 204600.4

## To retrofit D-G (mean per dwelling)

## [1] 14163.45

Table 4.13: Retrofit cost totals (£m GBP): All English LSOAs
meanPerLSOA_GBPm	total_GBPm
6.4	204,600.4

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.19 shows the LSOA level retofit costs per dwelling by IMD decile.

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.19: LSOA level retofit costs per dwelling by IMD score

4.4 Compare levy with costs

4.4.1 Scenario 1

Totals

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.4.2 Scenario 2

Totals

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Repeat per dwelling

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

4.5 Years to pay…

4.5.1 Scenario 1

Figure 4.20 shows years to pay under Scenario 1 (all emissions)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.09599  2.40262  3.17210  3.53055  4.44332 15.64765        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.20: Years to pay under Scenario 1 (all em issions)

## Median years: NA

Figure 4.21 shows years to pay under Scenario 1 (energy emissions)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.21: Years to pay under Scenario 1 (energy emissions)

## Median years: NA

Figure 4.22 shows the year 1 outcome if levy is shared equally (all emissions levy).

Figure 4.22: Year 1 outcome if levy is shared equally (all emissions levy)

Table 4.14: Highest retofit sum cost under Scenario 1
LSOA11CD	LSOA11NM	WD18NM	retrofitSum	yearsToPay	epc_D_pc	epc_E_pc	epc_F_pc	epc_G_pc
E01019012	Cornwall 054E	St Ives East	26,389,383.3	32.3	0.3	0.2	0.1	0.1
E01018781	Cornwall 034B	Rame Peninsular	22,060,171.6	49.7	0.3	0.3	0.2	0.1
E01027840	Scarborough 002C	Mulgrave	21,959,636.2	32.7	0.3	0.2	0.2	0.1
E01021988	Tendring 018A	Golf Green	21,701,516.6	36.6	0.3	0.3	0.2	0.1
E01018766	Cornwall 028D	Looe West, Lansallos and Lanteglos	21,409,249.4	46.6	0.2	0.3	0.3	0.1
E01020541	West Dorset 002C	Sherborne East	21,066,562.5	31.5	0.3	0.3	0.2	0.1
E01026741	North Norfolk 004A	High Heath	20,793,004.0	35.2	0.3	0.3	0.2	0.1
E01019002	Cornwall 070B	Newlyn and Mousehole	20,415,414.0	45.0	0.2	0.3	0.3	0.2
E01018982	Cornwall 057C	Hayle North	20,411,151.0	40.5	0.3	0.2	0.2	0.1
E01027374	Northumberland 003A	Bamburgh	19,563,518.6	30.0	0.3	0.2	0.1	0.1

Figure 4.23 shows the year 1 outcome if levy is shared equally (energy emissions levy).

Figure 4.23: Year 1 outcome if levy is shared equally (energy emissions levy)

What happens in Year 2 totally depends on the rate of upgrades… given the supply chain & capacity issues it’s likely that the levy would build up a substantial ‘headroom’ that could then be spent over time…

4.5.2 Scenario 2

Figure 4.24 shows years to pay under Scenario 2 (all emissions)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##  0.06524  2.83119  4.88954  5.92627  8.83376 31.42356        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.24: Years to pay under Scenario 2 (all em issions)

## Median years: NA

Figure 4.25 shows years to pay under Scenario 2 (energy emissions)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##   0.7742  14.7584  16.8635  17.5709  19.2684 118.6634        1

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Figure 4.25: Years to pay under Scenario 2 (energy emissions)

Figure 4.26 shows the year 1 outcome if levy is shared equally (all emissions levy).

Figure 4.26: Year 1 outcome if levy is shared equally (all emissions levy)

Figure 4.27 shows the year 1 outcome if levy is shared equally (energy emissions levy).

Figure 4.27: Year 1 outcome if levy is shared equally (energy emissions levy)

What happens in Year 2 totally depends on the rate of upgrades…

4.5.2.1 Compare scenarios

Figure 4.28 compares pay-back times for the two scenarios - who does the rising block tariff help?

Figure 4.28: Comparing pay-back times across scenarios

5 R environment

5.1 R packages used

base R (R Core Team 2016)
bookdown (Xie 2016a)
data.table (Dowle et al. 2015)
ggplot2 (Wickham 2009)
kableExtra (Zhu 2018)
knitr (Xie 2016b)
rmarkdown (Allaire et al. 2018)
skimr (Arino de la Rubia et al. 2017)

5.2 Session info

6 Data Tables

I don’t know if this will work…

## Doesn't

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for r. https://CRAN.R-project.org/package=rmarkdown.

Arino de la Rubia, Eduardo, Hao Zhu, Shannon Ellis, Elin Waring, and Michael Quinn. 2017. Skimr: Skimr. https://github.com/ropenscilabs/skimr.

Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.

R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.

Xie, Yihui. 2016a. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.

———. 2016b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://CRAN.R-project.org/package=knitr.

Zhu, Hao. 2018. kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.

Data notebook: Simulating a local emissions levy to fund local energy effiency retrofit

Focus on: All English LSOAs

Ben Anderson

Last run at: 2021-10-21 13:41:43